DETAILED ACTION
This office action is responsive to the above identified application filed 05/06/2025. The application contains claims 1-20, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim(s) 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-20 of U.S. Patent No. 12332917. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 12 and 17 of U.S. Patent No. 12332917 fully anticipate the claims 1, 12 and 17 of the instant application as shown in the chart below Dependent claims 2-11, 13-16 and 18-20 are rejected as being depending on rejected based claims.
Instant Application 19/200,455
US 12332917 B2
1. A computer-implemented method for segmenting univariate time series data comprising: generating multiple proxy variable time series for a univariate time series;
generating a supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series;
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series;
generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; and determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a similarity function that determines when changes between correlation values in graph objects meet or exceed a difference threshold.
12. A system comprising: a univariate time series;
a first sparse graph recovery model that generates graph objects from multiple portions of time series data;
a first similarity function that determines differences between two or more graph objects;
a processor; and a computer memory comprising instructions that, when executed by the processor, cause the system to carry out operations comprising: generating multiple proxy variable time series for the univariate time series;
generating a first supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series;
grouping portions of the first supplemented multivariate time series by a first window size to generate windowed subsequences of the first supplemented multivariate time series;
generating graph objects from the windowed subsequences utilizing the first sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; and determining one or more segmentation timestamps indicating one or more segment changes in the first supplemented multivariate time series utilizing the first similarity function that determines when changes between correlation values in graph objects meet or exceed a difference threshold.
17. A computer-implemented method for segmenting univariate time comprising: generating a proxy variable time series for a univariate time series;
generating a supplemented multivariate time series by supplementing the univariate time series with the proxy variable time series;
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series; generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a similarity function that determines when changes between correlation values in graph objects meet or exceed a difference threshold;
and generating a segmented univariate time series by segmenting the supplemented multivariate time series based on the one or more segmentation timestamps.
1. A computer-implemented method for segmenting univariate time series data comprising: generating multiple proxy variable time series for a univariate time series,
wherein a first proxy variable time series of the multiple proxy variable time series is based on the univariate time series; generating a supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series;
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series; generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; and determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a model conditioned on the univariate time series that determines when changes between correlation values in graph objects meet or exceed a difference threshold.
12. A system comprising: a univariate time series;
a first sparse graph recovery model that generates graph objects from multiple portions of time series data;
a first conditional similarity model that determines differences between two or more graph objects;
a processor; and a computer memory comprising instructions that, when executed by the processor, cause the system to carry out operations comprising: generating multiple proxy variable time series for the univariate time series;
generating a first supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series;
grouping portions of the first supplemented multivariate time series by a first window size to generate windowed subsequences of the first supplemented multivariate time series; generating graph objects from the windowed subsequences utilizing the first sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; and determining one or more segmentation timestamps indicating one or more segment changes in the first supplemented multivariate time series utilizing the first similarity model conditioned on the univariate time series that determines when changes between correlation values in graph objects meet or exceed a difference threshold.
17. A computer-implemented method for segmenting univariate time comprising: generating a proxy variable time series for a univariate time series;
generating a supplemented multivariate time series by supplementing the univariate time series with the proxy variable time series;
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series; generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a model conditioned on the univariate time series that determines when changes between correlation values in graph objects meet or exceed a difference threshold;
and generating a segmented univariate time series by segmenting the supplemented multivariate time series based on the one or more segmentation timestamps.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim(s) 1-11, 17-20 is/are method claims. Claim(s) 12-16 a system. Therefore, claims 1-20 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
Claim(s) 1, 12 and 17 recites the following limitation(s):
generating multiple proxy variable time series for a univariate time series; (Mathematical concept)
generating a supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series; (Mathematical concept)
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series; (Mathematical concept)
generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper with mathematical concept)
and determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a similarity function that determines when changes between correlation values in graph objects meet or exceed a difference threshold. (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper with mathematical concept)
Claim 12 additionally recite:
a univariate time series; (Mathematical concept)
a first sparse graph recovery model that generates graph objects from multiple portions of time series data; (Mathematical concept)
a first similarity function that determines differences between two or more graph objects; (Mathematical concept)
Claim 17 additionally recite:
and generating a segmented univariate time series by segmenting the supplemented multivariate time series based on the one or more segmentation timestamps (Mathematical concept)
Dependent claims 2-11, 13-16 and 18-20, are all evaluated similarly as the above limitations and also are directed to a mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper with mathematical concept
Accordingly, under its broadest reasonable interpretation, covers performance of the highlighted limitation(s) in the mind and/or using mathematical calculations but for the recitation of generic computer components. That is, other than reciting “computer-implemented,” “system,” “a processor,” nothing in the claim element(s) precludes the step(s) from practically being performed in the human mind using observation, evaluation, judgment, and opinion, and/or mathematical calculations. As such, the claim(s) falls within the “Mental Processes” and “Mathematical Concepts” grouping of abstract ideas. Therefore, the claim(s) recites an abstract idea.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application. The claim(s) recites the following additional elements:
Claims 1 and 17 recites: computer-implemented method; Claim 12, recites: system, and processor; (all of which are recited at high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer)
No other additional elements were identified. Accordingly, claims 1-120 does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim(s) are directed to an abstract idea.
Step 2B: The claim(s) does not include additional element(s) that are sufficient to amount to significantly more than the judicial exception. Therefore, the additional element(s) are not indicative of an inventive concept (aka “significantly more”). The claim(s) are not patent eligible.
Allowable Subject Matter
Claim(s) 5, 11, {13-16} are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and the above Double Patenting and 35 USC § 101 rejections are resolved.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 10, 12 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwayama et al. “Change-point detection with recurrence networks”; Nonlinear Theory and Its Applications IEICE 4(2):160-171; DOI:10.1587/nolta.4.160; January 2013, in view of Hallac et al. “Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data”; arXiv:1706.03161v2 [cs.LG] 15 May 2018, and Yang et al. (US 20210357431 A1).
As per claim 1, Iwayama discloses:
a computer-implemented method for segmenting univariate time series data comprising: generating multiple proxy variable time series for a univariate time series, at least by (Sec. 2.2, describes the reconstructing the state space…v(t), EQ (3) (e.g. multiple proxy variable time series) from an observed time series x(t) (e.g. a univariate time series; see also Sec. 3, describing numerical simulations observe “a univariate time series” and reconstruct state vectors))
generating a supplemented multivariate time series by , at least by (Sec. 2.2, by describes delay-coordinate vectors include current and delayed scalar vector as a multidimensional representation, and describing reconstructing the state space…v(t) teaches “supplementing” the state space)
generating graph objects from the windowed subsequences
and determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series at least by (Sec. 2.4, which describes detecting change-points (e.g. segmentation timestamps indicating one or more segment changes) by applying spectral clustering to recurrence networks and confirms significance by comparing eigenvalues to surrogate data)
As shown above Iwayama fails to specifically describe:
(a) “supplementing” the univariate time series with proxy variable time series.
(b) grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series,
(c) utilizing a sparse graph recovery model, to generate the graph objects
(d) utilizing a similarity function that determines when changes between correlation values in graph objects meet or exceed a difference threshold
However, Hallac teaches the above limitation (a) at least by (Sec. 2, describes how windowed multivariate representations are supplied downstream and each observation subsequence xt (e.g. univariate time series) is concatenated into nw dimensional vector, (e.g. supplementing the univariate time series with proxy variable time series)
Also Hallac teaches the above limitation (b) at least by (Sec. 2, describes clustering a short subsequence of size w ending at time t, consisting of observations xt−w+1, . . . , xt, which further describes segmenting based on timestamps).
Also Hallac teaches the above limitation (c) at least by (Sec. 2, describes defining each cluster by Gaussian inverse covariance and solves Toeplitz graphical lasso with sparsity penalty (e.g. sparse graph recovery model)).
And, Yang teaches the above limitation (d) at least by (paragraph [0027, 0028] describing calculating similarity scores between time-series subgraphs and compares them to a similarity threshold)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Hallac ability to cluster time series data, characterizing the interdependencies between different observations in a typical subsequence of that cluster via TICC to learn interpretable clusters in real-world scenarios (Hallac, Abstract) and to further combine with Yang to accurately capture characteristics in the given time series data such as periodicity or trending characteristics (Yang, para. 0019).
As per claim 3, claim 1 is incorporated and Iwayama further describes:
further comprising generating a segmented univariate time series by segmenting the supplemented multivariate time series based on the one or more segmentation timestamps, at least by (Sec. 2.4, which describes detecting change-points (e.g. segmentation timestamps indicating one or more segment changes) by applying spectral clustering to recurrence networks and confirms significance by comparing eigenvalues to surrogate data)
Also Hallac teaches segmenting… based on one or more segmentation timestamps at least by (Sec. 2, describes clustering a short subsequence of size w ending at time t, consisting of observations xt−w+1, . . . , xt, which further describes segmenting based on timestamps).
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Hallac ability to cluster time series data, characterizing the interdependencies between different observations in a typical subsequence of that cluster via TICC to learn interpretable clusters in real-world scenarios (Hallac, Abstract).
As per claim 10, claim 1 is incorporated and Iwayama further discloses:
further comprising: comparing a first graph object to a second graph object utilizing the similarity function to determine that a difference between the first graph object and the second graph object satisfies a difference threshold; and determining a first segmentation timestamp based on a segmentation timestamp of the first graph object, at least by (Sec. 2.4, which describes comparing graph derived clustering/eigenvalue to detect change points, but fails to specifically describes comparing a first graph object to a second graph object utilizing the similarity function to determine that a difference between the first graph object and the second graph object satisfies a difference threshold; and determining a first segmentation timestamp based on a segmentation timestamp of the first graph object)
However, Yang teaches the above limitation, at least by (paragraph [0027, 0028] describing calculating similarity scores between time-series subgraphs and compares them to a similarity threshold to identify classification segments)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Yang to accurately capture characteristics in the given time series data such as periodicity or trending characteristics (Yang, para. 0019).
Claim(s) 12 recite equivalent claim limitations as claim(s) 1 above, except that they set forth the claimed invention as a system; Claim(s) 17 recite equivalent claim limitations as claim(s) 1 and 3 above, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 2, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwayama, Hallac and Yang further in view of Courtney et al. (US 20220383033 A1).
As per claim 2, claim 1 is incorporated and Iwayama fails to describe:
further comprising generating a first proxy variable time series of the multiple proxy variable time series by interpolating the univariate time series at a first sampling rate.
However, Courtney teaches the above limitation at least by (paragraph [0027] describes adjusting sampling rates using interpolation, generating additional points between existing points, and identifying/up-sampling streams based on sampling rates)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Courtney sampling interpolation logic to generate time-series data streams having uniform sampling rates (Courtney, para. 0027).
As per claim 18, claim 17 is incorporated and Iwayama fails to disclose:
generating a first proxy variable time series by interpolating the univariate time series at a first sampling rate; and generating a second proxy variable time series by interpolating the univariate time series at a second sampling rate that is different from the first sampling rate.
However, Courtney teaches the above limitation at least by (paragraph [0027] describes adjusting sampling rates using interpolation, generating additional points between existing points, and identifying/up-sampling streams based on sampling rates, which describes multiple different sampling rates for different interpolated streams)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Courtney sampling interpolation logic to generate time-series data streams having uniform sampling rates (Courtney, para. 0027).
As per claim 19, claim 17 is incorporated and Iwayama fails to disclose:
further comprising generating an additional proxy variable time series by interpolating the univariate time series.
However, Courtney teaches the above limitation at least by (paragraph [0027] describes adjusting sampling rates using interpolation, generating additional points between existing points, and identifying/up-sampling streams based on sampling rates)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Courtney sampling interpolation logic to generate time-series data streams having uniform sampling rates (Courtney, para. 0027).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwayama, Hallac and Yang further in view of Pallath et al. (US 20180150547 A1.)
As per claim 4, claim 1 is incorporated and Iwayama fails to describe:
further comprising: determining a data type of the univariate time series; and determining the multiple proxy variable time series based on the data type.
However, Pallath teaches the above limitation at least by (paragraph [0024] describes identifying and labeling common types of patterns in time series which describes determining a data type of the univariate time series and further describes “ a dimension reduction algorithm is applied to transform the time sequences to a new feature space with lower dimensions… the original time series is divided into equidistant time windows and the dimension is reduced by replacing all observations in the same time window with the calculated mean values.” see para. 0022, 0026, is equivalent to determining the multiple proxy variable time series based on the data type)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Pallath improve the ease and efficiency of time series data mining (Pallath , para. 0022).
Claim(s) 6, 7, 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwayama, Hallac and Yang further in view of Glad et al. “GLAD: Learning Sparse Graph Recovery” arXiv:1906.00271v3 [cs.LG] 21 Dec 2019.
As per claim 6, claim 1 is incorporated and Iwayama fails to describe:
further comprising: generating additional graph objects from the windowed subsequences utilizing an additional sparse graph recovery model that is different from the sparse graph recovery model; and determining that the graph objects and the additional graph objects are within a threshold similarity.
However, Glad describes the use of an additional sparse graph recovery model that is different from the sparse graph recovery model for generating additional graph objects at least by (Abstract, where sparce independence graphs are extracted from data (e.g. additional graph objects) using empirical covariance/alternating minimization algorithm (e.g. additional sparse graph recovery model)
And, Yang teaches the above limitation determining that the graph objects and the additional graph objects are within a threshold similarity at least by (paragraph [0027, 0028] describing calculating similarity scores between time-series subgraphs and compares them to a similarity threshold)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Glad’s to recover sparse conditional independence graphs from data (Glad’s, Abstract) and to further combine with Yang to accurately capture characteristics in the given time series data such as periodicity or trending characteristics (Yang, para. 0019).
As per claim 7, claim 1 is incorporated and Iwayama fails to describe:
wherein generating the graph objects from the windowed subsequences includes generating a visual graph of nodes and edges, where the edges indicate a positive or negative partial correlation between connected nodes.
However, Glad discloses the above limitation at least by (Fig. 7 which shows the visual graph of nodes and edges where green edges indicate positive correlation and red edges indicate negative correlation between the connected nodes.)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Glad’s to recover sparse conditional independence graphs from data (Glad’s, Abstract).
As per claim 8, claim 1 is incorporated and Iwayama fails to describe:
further comprising generating multiple graph objects from the windowed subsequences at a same time as part of a batch operation that utilizes one instance of the sparse graph recovery model and shared parameters.
However, Glad, discloses the above limitation at least by (Sec. 3, describes windowed subsequences as “samples can be used to form n sample covariance matrices” and using the “algorithm GLAD (stands for Graph recovery Learning Algorithm using Data-driven training” where f is a set of parameters in GLAD(.)” see corresponding formula in Sec. 3, as sparse graph recovery model and shared parameters to output multiple graph objects from the windowed subsequences at a same time as part of a batch operation)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Glad’s to recover sparse conditional independence graphs from data (Glad’s, Abstract).
As per claim 9, claim 1 is incorporated and Iwayama fails to describe:
wherein the sparse graph recovery model generates conditional independence graph objects that exhibit partial correlation between variables.
However, Glad, discloses the above limitation at least by (Abstract, describes recovering sparse conditional independence graphs” and mapping empirical covariance to space precision matrix)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Glad’s to recover sparse conditional independence graphs from data (Glad’s, Abstract).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwayama, Hallac and Yang further in view of D. Guijo-Rubio et al., "Time-Series Clustering Based on the Characterization of Segment Typologies," in IEEE Transactions on Cybernetics, vol. 51, no. 11, pp. 5409-5422, Nov.2021.
As per claim 20, claim 17 is incorporated and Iwayama fails to disclose:
wherein the proxy variable time series is a polynomial proxy variable.
However, Guijo teaches the above limitation at least by (Abstract, Sec. II.C.2., “simultaneously obtaining a segmentation of the time series and the coefficients of the polynomial least-squares approximation”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Iwayama with Guijo’s time series segmentation using polynomial coefficients of the least-squares approximation to reduce errors while choosing segmentation window size (Guijo, Sec. III.A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nagar et al. (US 20240054041 A1): Abstract, para. 0018, 0023, 0034-0038
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/DENNIS TRUONG/Primary Examiner, Art Unit 2164 05/14/2026
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